python-fake-data-producer-for-apache-kafka VS Grafana

I had good experience with Aiven in the past, we needed something located in the EU: https://aiven.io/

Access to Aiven

One of the hardest questions we answer for our large enterprise customers is why they should choose Aiven instead of managing their own database and streaming services. It can seem counterintuitive that paying extra for a managed service can save you money. However, when we factor in economies of scale - particularly in regards to access to specialized knowledge and tooling - the case for managed services becomes clear. This was certainly the case for some of our MySQL clients earlier this year, where their investments in Aiven paid off in the form of a quietly managed bug fix.

And Kafka + Kafka Connect has https://www.confluent.io/ https://aiven.io/ https://upstash.com/ (and not quite Kafka, but protocol-compatible, https://redpanda.com/)

Fake data utility - https://github.com/aiven/python-fake-data-producer-for-apache-kafka

For PostgreSQL, similar offerings are: * Google Cloud SQL * AWS RDS * Digital Ocean Postgres * Azure Database for Postgres * Aiven, Instaclustr etc

Aiven - https://aiven.io/

Majority of companies layer DevRel on top of marketing as a sort of afterthought, and that’s usually a recipe for failure. All four co-founders at Aiven (the company where I work at) have been long-time open-source maintainers/contributors and highly value the work of DevRel. Similar examples can be seen at HashiCorp, where co-founder Armon Dadgar has been doing DevRel on the whiteboard since the early days of the company. Technical founders know the value of DevRel and know when to form a DevRel team. This is very different from bringing your first DevRel hire onboard and making them convince the leadership why the company needs DevRel in the first place. If your developer advocate needs to explain to the technical leadership the need for DevRel, that's a red flag for that individual and the company.

I wonder when Aiven https://aiven.io/ (or something similar) will start supporting hetzner.

Monitoring application logs is a crucial aspect of the software development and deployment lifecycle. In this post, we'll delve into the process of observing logs generated by Docker container applications operating within HashiCorp Nomad. With the aid of Grafana, Vector, and Loki, we'll explore effective strategies for log analysis and visualization, enhancing visibility and troubleshooting capabilities within your Nomad environment.

To help us visualize these scenarios, we'll build a Grafana Dashboard so we can follow along.

Visualization and Analysis: Choose a tool with intuitive and customizable dashboards, charts, and visualizations. A question to ask is, "Are the visualization features of this tool user-friendly and adaptable to our team's specific needs?" Tools like Grafana and Kibana provide powerful visualization capabilities.

Prometheus: Open-source monitoring system. Often used together with Grafana.

Grafana

Yes I'm aware that Grafana is open source but the method I used to find the API endpoints is far quicker than digging through hundreds of files in a codebase I'm not familiar with.

So, you will add one last container to allow us to visualize this data: Grafana, an open-source analytics and visualization platform that allows us to see traces and metrics simply. You can set Grafana to read data from both Tempo and Prometheus by setting them as datastores with the following grafana.datasource.yaml config file:

In example above, we use 2 additional parameters: code (HTTP response code) and page (page identifier), which provide detailed statistics. For example, you can build such graphs in Grafana:

Robin switched to the Grafana dashboard tab, and sure enough, the 5xx volume on web service was rising. It had not hit the critical alert thresholds yet, but customers had already started noticing.